Resource Profile: The Regenstrief Institute COVID-19 Research Data Commons (CoRDaCo)

The primary objective of the COVID-19 Research Data Commons (CoRDaCo) is to provide broad and efficient access to a large corpus of clinical data related to COVID-19 in Indiana, facilitating research and discovery. This curated collection of data elements provides information on a significant portion of COVID-19 positive patients in the State from the beginning of the pandemic, as well as two years of health information prior its onset. CoRDaCo combines data from multiple sources, including clinical data from a large, regional health information exchange, clinical data repositories of two health systems, and state laboratory reporting and vital records, as well as geographic-based social variables. Clinical data cover information such as healthcare encounters, vital measurements, laboratory orders and results, medications, diagnoses, the Charlson Comorbidity Index and Pediatric Early Warning Score, COVID-19 vaccinations, mechanical ventilation, restraint use, intensive care unit and ICU and hospital lengths of stay, and mortality. Interested researchers can visit ridata.org or email [email protected] to discuss access to CoRDaCo.

A Topography of Data Commons: From Regulation to Private Dynamism

Knowledge is subject to enclosure through digital technology and legal rules. Data collected, stored and pooled by the Internet of Things (IoT) or Artificial Intelligence (AI) are no exception to this. Operators acting in the markets related to the algorithmic society may have a quite diversified range of intellectual property rights (IPRs) to protect the information they produce and manage. This is exploited through algorithmic processing techniques, aggregating collected data for the generation of new ones, thus creating additional information and knowledge. This paper studies whether and when data, information and knowledge, presented within the Big Data, IoT and AI structures, may be considered and exploited as commons. The analysis is not aimed at stating that commons should be the general solution for the algorithmic society. Nor does it endorse legal interpretations unilaterally favoring openness and limiting IPR protection and privacy rules (though this could be the case under certain circumstances). The question is to establish whether a certain level of commons should be provided by regulation or left to spontaneous private initiatives. In this regard, two different meanings of data commons are used in this work. The first one refers to the open access systems provided by regulation, equivalent to a public domain protection, and opposed to exclusivity mechanisms. The second refers to data commons which are privately ‘constructed’ on top of background regulation and manage resources for a limited set of claimants.

From biobank and data silos into a data commons: convergence to support translational medicine

Background To drive translational medicine, modern day biobanks need to integrate with other sources of data (clinical, genomics) to support novel data-intensive research. Currently, vast amounts of research and clinical data remain in silos, held and managed by individual researchers, operating under different standards and governance structures; a framework that impedes sharing and effective use of data. In this article, we describe the journey of British Columbia’s Gynecological Cancer Research Program (OVCARE) in moving a traditional tumour biobank, outcomes unit, and a collection of data silos, into an integrated data commons to support data standardization and resource sharing under collaborative governance, as a means of providing the gynecologic cancer research community in British Columbia access to tissue samples and associated clinical and molecular data from thousands of patients. Results Through several engagements with stakeholders from various research institutions within our research community, we identified priorities and assessed infrastructure needs required to optimize and support data collections, storage and sharing, under three main research domains: (1) biospecimen collections, (2) molecular and genomics data, and (3) clinical data. We further built a governance model and a resource portal to implement protocols and standard operating procedures for seamless collections, management and governance of interoperable data, making genomic, and clinical data available to the broader research community. Conclusions Proper infrastructures for data collection, sharing and governance is a translational research imperative. We have consolidated our data holdings into a data commons, along with standardized operating procedures to meet research and ethics requirements of the gynecologic cancer community in British Columbia. The developed infrastructure brings together, diverse data, computing frameworks, as well as tools and applications for managing, analyzing, and sharing data. Our data commons bridges data access gaps and barriers to precision medicine and approaches for diagnostics, treatment and prevention of gynecological cancers, by providing access to large datasets required for data-intensive science.

Decreased ARID1A Expression Associates with Tumor Progression and Adverse Prognosis in Renal Cell Carcinoma

In this study, we aimed to evaluate association of ARID1A (AT-rich interacting domain-containing protein 1A) mutation and protein expression with clinicopathology and prognosis of renal cell carcinoma (RCC). Genomic Data Commons (GDC) showed ARID1A was one of the top-ten mutated genes found in kidney cancers and its mutations were found along its sequence. Interestingly, patients with ARID1A mutations had significantly lower survival rate (38%; n=68) comparing to the non-mutated cases (58%; n=192). The results from OSkirc web tool revealed that patients with low expression of ARID1A had significantly shorter overall survival and disease specific survival than those with high ARID1A expression. Immunohistochemistry revealed markedly decreased ARID1A expression in the RCC tissues (n=26), particularly in clear cell RCC (ccRCC) and chromophobe RCC (chRCC). Negative to weak ARID1A expression was significantly associated with ccRCC (grade II) and chRCC subtypes, presence of comorbidity, and low eGFR levels. Finally, ARID1A protein was undetectable in 3/11 cases with ccRCC (grade II) and 2/6 chRCC cases, all of which had metastasis 1−50 months after surgical removal. In conclusion, decreased ARID1A expression is associated with the poor prognosis and metastasis of RCC and thus may serve as the prognostic marker of RCC, particularly ccRCC and chRCC subtypes.

From biobank and data silos into a data commons: convergence to support translational medicine

Background To drive translational medicine, modern day biobanks need to integrate with other sources of data (clinical, genomics) to support novel data-intensive research. Currently, vast amounts of research and clinical data remain in silos, held and managed by individual researchers, operating under different standards and governance structures; a framework that impedes sharing and use of data. In this article, we describe the journey of British Columbia’s Gynecological Cancer Research Program (OVCARE) in moving a traditional tumour biobank, outcome unit, and a collection of data silos, into an integrated data commons to support data standardization, data, and resources sharing under collaborative governance, as a means of providing the gynecologic cancer research community in British Columbia access to tissue samples and associated clinical and molecular data from thousands of patients. Results Through several engagements with stakeholders from various research institutions within our research community, we identified priorities and assessed infrastructure needs required to optimize and support data collections, storage and sharing, under three main research domains: 1) biospecimen collections, 2) molecular and genomics data, and 3) clinical data. We further built a governance model and a resource portal to implement protocols and standard operating procedures for seamless collections, management and governance of interoperable data, making genomic, and clinical data available to the broader research community. Conclusions Proper infrastructures for data collection, sharing and governance is a translational research imperative. We have consolidated our data holdings into a data commons, along with standardized operating procedures to meet research and ethics requirements of the gynecologic cancer community in British Columbia. The developed infrastructure brings together, diverse data, computing framework, as well as tools and applications for managing, analyzing, and sharing data. Our data commons bridges data access gaps and barriers to precision medicine and approaches for diagnostics, treatment and prevention of gynecological cancers, by providing access to large datasets required for data-intensive science.

Biomedical Data Commons (BMDC) prioritizes B-lymphocyte non-coding genetic variants in Type 1 Diabetes

The repurposing of biomedical data is inhibited by its fragmented and multi-formatted nature that requires redundant investment of time and resources by data scientists. This is particularly true for Type 1 Diabetes (T1D), one of the most intensely studied common childhood diseases. Intense investigation of the contribution of pancreatic β-islet and T-lymphocytes in T1D has been made. However, genetic contributions from B-lymphocytes, which are known to play a role in a subset of T1D patients, remain relatively understudied. We have addressed this issue through the creation of Biomedical Data Commons (BMDC), a knowledge graph that integrates data from multiple sources into a single queryable format. This increases the speed of analysis by multiple orders of magnitude. We develop a pipeline using B-lymphocyte multi-dimensional epigenome and connectome data and deploy BMDC to assess genetic variants in the context of Type 1 Diabetes (T1D). Pipeline-identified variants are primarily common, non-coding, poorly conserved, and are of unknown clinical significance. While variants and their chromatin connectivity are cell-type specific, they are associated with well-studied disease genes in T-lymphocytes. Candidates include established variants in the HLA-DQB1 and HLA-DRB1 and IL2RA loci that have previously been demonstrated to protect against T1D in humans and mice providing validation for this method. Others are included in the well-established T1D GRS2 genetic risk scoring method. More intriguingly, other prioritized variants are completely novel and form the basis for future mechanistic and clinical validation studies The BMDC community-based platform can be expanded and repurposed to increase the accessibility, reproducibility, and productivity of biomedical information for diverse applications including the prioritization of cell type-specific disease alleles from complex phenotypes.

Building a Global Open Extensible Biodiversity Commons Network

Recent global events reinforce the need for local to global coalitions to address a variety of socio-environmental challenges such as the current COVID-19 pandemic (Cook et al. 2020) and biodiversity loss in general. Scientists reviewing data and fitness for current and future use note urgent necessary changes needed in data collection, specimen collection and preservation, infrastructure, human capacity, and standards-of-practice (Raven and Miller 2020, Morrison et al. 2017, Cook et al. 2020). Multi-faceted research questions often require cross-disciplinary collaboration. A recent paper analyzed conservation and disease mitigation research author networks and discovered that certain disciplines do not work together unless the research has outcomes that serve all groups involved (Kading and Kingston 2020). This research reinforces the finding that common goals offer a powerful way to build effective cross-disciplinary networks, speed up collaboration, and more effectively take on complex research. To move toward a Digital Extended Specimen (DES), the alliance for biodiversity knowledge is engaging in community building. The above summary when coupled with conversations from our alliance-led online consultations reinforces known threads and reveals some emerging themes about partnerships and collaborations. Our group continues to work on defining what a Digital Specimen is (or is not) and then communicating that succinctly to the worldwide community. At the same time we recognize the need for an extensible digital specimen object, we note the need for an extensible network. We note that groups need to and are motivated to solve local issues (as in for their town, or their country or continent). So, looking for and selecting common threads across these regional scales will be key to realizing and motivating effective partnerships and networks. Foremost, this includes expanding participation beyond Europe and North America. We recognize the need to form new partnerships to expand our network and learn from our new partners. For example, the Digital Humanities community would like to talk about the intersection of the humanities, social sciences, biology, and collections that can help each other to do better research. With this talk, and through participation in TDWG2021, we seek to share information and insights gathered so far about next steps and about building and sustaining the network we need to realize a biodiversity data commons and get input from those who participate in our session.

Pediatric Cancer Data Commons: Federating and Democratizing Data for Childhood Cancer Research

The international pediatric oncology community has a long history of research collaboration. In the United States, the 2019 launch of the Children's Cancer Data Initiative puts the focus on developing a rich and robust data ecosystem for pediatric oncology. In this spirit, we present here our experience in constructing the Pediatric Cancer Data Commons (PCDC) to highlight the significance of this effort in fighting pediatric cancer and improving outcomes and to provide essential information to those creating resources in other disease areas. The University of Chicago's PCDC team has worked with the international research community since 2015 to build data commons for children's cancers. We identified six critical features of successful data commons design and implementation: (1) establish the need for a data commons, (2) develop and deploy the technical infrastructure, (3) establish and implement governance, (4) make the data commons platform easy and intuitive for researchers, (5) socialize the data commons and create working knowledge and expertise in the research community, and (6) plan for longevity and sustainability. Data commons are critical to conducting research on large patient cohorts that will ultimately lead to improved outcomes for children with cancer. There is value in connecting high-quality clinical and phenotype data to external sources of data such as genomic, proteomics, and imaging data. Next steps for the PCDC include creating an informed and invested data-sharing culture, developing sustainable methods of data collection and sharing, standardizing genetic biomarker reporting, incorporating radiologic and molecular analysis data, and building models for electronic patient consent. The methods and processes described here can be extended to any clinical area and provide a blueprint for others wishing to develop similar resources.